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Table 2 Semantic segmentation results on the Semantic3D dataset

From: Pseudo-labelling-aided semantic segmentation on sparsely annotated 3D point clouds

Method Overall accuracy (%) Average F-score (%) Per-class F-scores(%)
    Terrain Vegetation Building Hardscape Artefacts Cars
Pointwise [1] 49.5 29.3 87.2 13.6 59.0 12.9 2.2 1.0
CRF-reg [22] 65.0 42.8 96.2 32.0 73.5 28.4 24.8 1.8
Seg-aided [17] 74.4 43.1 95.7 28.1 83.0 24.7 22.9 4.0
Supervised baseline 86.2 51.9 97.1 36.5 91.7 66.3 6.7 13.0
Ours no kdist 88.1 56.9 97.7 51.8 92.7 54.9 4.8 39.2
Ours with kdist 95.6 66.7 94.2 61.2 97.7 84.6 9.0 53.3